SOT/OTS Monthly Notebook

This notebook documents the process for running the SOT/OTS monthly scripts in R. Preceeding these scripts is a SQL procedure and the end output is a collection of .csv files. The following document will demonstrate the process for creating the Master tables and also the output of a single .csv file.

First we need to load our libraries.

library(dplyr)
library(readr)
library(RODBC)
library(formattable)
library(RJDBC)
library(rChoiceDialogs)

Then set up our Environment with a few functions:

# setup environment ----
prompt_for_week <- function()
{ 
  n <- readline(prompt="Enter Week number: ")
  return(as.integer(n))
}
choose_file_directory <- function()
{
  v <- jchoose.dir()
  return(v)
}

The following code will open a system-independent, file chooser using Java and the function created above. It will allow you to choose the directory in which to save all files. This will sometimes fail the first time it is run. If it fails just rerun it (usualy works the second time). Note: Eval is set to off to disable dynamic functionality for this notebook. This notebook will use the current working directory instead.

SOT_OTS_directory <- choose_file_directory()

EOW <- prompt_for_week()

Next we need to create a connection to EDWP. Once you have stored your username and password as my_uid and my_pwd; and created a DSN, connect with a string similar to this (You may need to change the below if you gave your DSN an differnet name). Then verify that we have succesfully connected by performing a query on the dbcinfo table.

# Create RODBC connection---- 
my_connect <- odbcConnect(dsn= "IP EDWP", uid= my_uid, pwd= my_pwd)
# sqlTables(my_connect, catalog = "EDWP", tableName  = "tables")
sqlQuery(my_connect, query = "SELECT  * from dbc.dbcinfo;")

We can see that the server has returned our table! We have connected!

Next we need to create our master tables via query and store them as R objects.

# Query EDW ----
SOT_Master <- sqlQuery(my_connect, 
                       query = "SELECT  * from SRAA_SAND.VIEW_SOT_MASTER;")

OTS_Master <- sqlQuery(my_connect, 
                       query = "SELECT  * from SRAA_SAND.VIEW_OTS_MASTER;")
close(my_connect)

For the purpose of this notebook, we will not query the database however. Instead, we will load objects already created and stored on an FTP site on the corprate network.

Now let’s load a few static files that will we need for mapping and save the master objects we just created.

# Import static files ----
Preferred_Vendor_new <- read_delim(file = "ftp://ftp.gap.com/data/to_hq/SupplyChainReporting/GSCAT/SOTMasterObjects/Preferred Vendor (new).csv", 
                                   delim = "^")
Country_description <- read_delim(file= "ftp://ftp.gap.com/data/to_hq/SupplyChainReporting/GSCAT/SOTMasterObjects/Country Description.txt", 
                                  delim = "^")
# save Master Objects ----
save(SOT_Master, file = paste(SOT_OTS_directory,  'SOT_Master_object.rtf', sep = .Platform$file.sep))
save(OTS_Master, file = paste(SOT_OTS_directory,  'OTS_Master_object.rtf', sep = .Platform$file.sep ))

And create a few tables for joining.

# Create TOP 20 Countries Table ----
Top_20_Countries <- left_join(SOT_Master, Country_description, by= c("CountryOfOrigin"="CTRY_CD" ))
Top_20_Countries <- Top_20_Countries %>% 
  group_by(CountryOfOrigin, CTRY_DESC) %>% 
  summarise("Units by Country" = floor(sum(Units))) %>% 
  arrange(desc(`Units by Country`)) %>% 
  head(20) 
# Create top 50 Vendors ----
Top_50_Vendors <- SOT_Master %>% 
  group_by(Parent_Vendor) %>% 
  summarise("Units by Vendor" = floor(sum(Units))) %>% 
  arrange(desc(`Units by Vendor`)) %>% 
  head(50)

Check the first 6 rows of SOT Master to test that our load() was successsful:

head(SOT_Master)

Once we have our master tables as R objects we need to clean them up a bit:

# Remove noise from OTS and SOT Master
OTS_Master <- OTS_Master %>% 
  filter(Week <= EOW,
         !grepl("Liberty Distribution", Parent_Vendor, ignore.case = TRUE),
         !grepl("dummy", Parent_Vendor, ignore.case = TRUE),
         !grepl("JPF", DC_NAME, ignore.case = TRUE)) 

SOT_Master <- SOT_Master %>% 
  filter(ShipCancelWeek <= EOW,
         !grepl("Liberty Distribution", Parent_Vendor, ignore.case = TRUE),
         !grepl("dummy", Parent_Vendor, ignore.case = TRUE),
         MetricShipDate <= Sys.Date()) 

In the actual script Master_Import.R there are many tables that are generated from the above Master tables. The following code is an example of one output that will illustrate the process for all. The remaining code will be reserved for the appendix of this document.

Monthly_Brand_Category_SOT <- SOT_Master %>%
  filter(SOT_Master$ShipCancelWeek <= EOW) %>%
  group_by(ShipCancelMonth, ReportingBrand, Category) %>% 
  summarise("SOTUnits" = floor(sum(Units)),
            "SOTOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "SOTLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "SOTLate5daysUnits" = floor(sum(Units[Lateness=="Late" & DAYS_LATE > 5])), 
            "WTSOTLateUnits" = floor(sum(Units[Lateness=="Late"]*DAYS_LATE[Lateness=="Late" & DAYS_LATE >=1])),
            "PPAUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir"])),
            "PPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"])), 
            "PPASOT5daysLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE>5])),
            "WTPPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]*DAYS_LATE[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE >=1]))) %>%  
  select(ShipCancelMonth, 
         ReportingBrand, 
         Category, 
         SOTUnits, 
         SOTOnTimeUnits, 
         SOTLateUnits, 
         SOTLate5daysUnits, 
         WTSOTLateUnits, 
         PPAUnits,
         PPASOTLateUnits,
         PPASOT5daysLateUnits,
         WTPPASOTLateUnits)

Taking a look at our new table:

Lastly we need to output this file as a csv. The file will be output to the directory you set up using the Java file chooser earlier (deactivated for this notebook).

write_csv(Monthly_Brand_Category_Combine, 
          path = paste(SOT_OTS_directory,  
                       paste('Monthly_Brand_Category_Combine_WE_', EOW, '.csv',sep = ""), sep = '/' ))

That is it. The basic process is complete. The remaining tables can be created in much the same way.

Appendix

# Create Monthly SOT Brand Table ----
Monthly_Brand_SOT <- SOT_Master %>%
  filter(SOT_Master$ShipCancelWeek <= EOW) %>%
  group_by(ShipCancelMonth, ReportingBrand) %>% 
  summarise("SOTUnits" = floor(sum(Units)),
            "SOTOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "SOTLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "SOTLate5daysUnits" = floor(sum(Units[Lateness=="Late" & DAYS_LATE > 5])), 
            "WTSOTLateUnits" = floor(sum(Units[Lateness=="Late"]*DAYS_LATE[Lateness=="Late" & DAYS_LATE >=1])),
            "PPAUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir"])),
            "PPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"])), 
            "PPASOT5daysLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE>5])),
            "WTPPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]*DAYS_LATE[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE >=1]))) %>%  
  select(ShipCancelMonth, 
         ReportingBrand, 
         SOTUnits, 
         SOTOnTimeUnits, 
         SOTLateUnits, 
         SOTLate5daysUnits, 
         WTSOTLateUnits, 
         PPAUnits,
         PPASOTLateUnits,
         PPASOT5daysLateUnits,
         WTPPASOTLateUnits)
# View(Monthly_Brand_SOT)

# Create Monthly SOT Category Table ----
Monthly_Category_SOT <- SOT_Master %>%
  filter(SOT_Master$ShipCancelWeek <= EOW) %>%
  group_by(ShipCancelMonth, Category) %>% 
  summarise("SOTUnits" = floor(sum(Units)),
            "SOTOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "SOTLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "SOTLate5daysUnits" = floor(sum(Units[Lateness=="Late" & DAYS_LATE > 5])), 
            "WTSOTLateUnits" = floor(sum(Units[Lateness=="Late"]*DAYS_LATE[Lateness=="Late" & DAYS_LATE >=1])),
            "PPAUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir"])),
            "PPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"])), 
            "PPASOT5daysLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE>5])),
            "WTPPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]*DAYS_LATE[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE >=1]))) %>%  
  select(ShipCancelMonth, 
         Category, 
         SOTUnits, 
         SOTOnTimeUnits, 
         SOTLateUnits, 
         SOTLate5daysUnits, 
         WTSOTLateUnits, 
         PPAUnits,
         PPASOTLateUnits,
         PPASOT5daysLateUnits,
         WTPPASOTLateUnits)
# View(Monthly_Category_SOT) 

# Create Monthly Gap Inc SOT Table ----
Monthly_GapInc_SOT <- SOT_Master %>%
  filter(SOT_Master$ShipCancelWeek <= EOW) %>%
  group_by(ShipCancelMonth) %>% 
  summarise("SOTUnits" = floor(sum(Units)),
            "SOTOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "SOTLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "SOTLate5daysUnits" = floor(sum(Units[Lateness=="Late" & DAYS_LATE > 5])), 
            "WTSOTLateUnits" = floor(sum(Units[Lateness=="Late"]*DAYS_LATE[Lateness=="Late" & DAYS_LATE >=1])),
            "PPAUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir"])),
            "PPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"])), 
            "PPASOT5daysLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE>5])),
            "WTPPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]*DAYS_LATE[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE >=1]))) %>%  
  select(ShipCancelMonth, 
         SOTUnits, 
         SOTOnTimeUnits, 
         SOTLateUnits, 
         SOTLate5daysUnits, 
         WTSOTLateUnits, 
         PPAUnits,
         PPASOTLateUnits,
         PPASOT5daysLateUnits,
         WTPPASOTLateUnits)
# View(Monthly_GapInc_SOT) 

# Create Monthly OTS Brand and Category Table ----
Monthly_Brand_Category_OTS <- OTS_Master %>%
  filter(OTS_Master$Week <= EOW) %>%
  group_by(Month_Number, ReportingBrand, Category) %>% 
  summarise("OTSUnits" = floor(sum(Units)),
            "OTSOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "OTSLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "OTSLate5daysUnits" = floor(sum(Units[Lateness=="Late" & Days_Late > 5])), 
            "WTOTSLateUnits" = floor(sum(Units[Lateness=="Late"]*Days_Late[Lateness=="Late" & Days_Late >=1])),
            "PPAOTSLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]))) %>%  
  select(Month_Number, 
         ReportingBrand, 
         Category, 
         OTSUnits, 
         OTSOnTimeUnits, 
         OTSLateUnits, 
         OTSLate5daysUnits, 
         WTOTSLateUnits, 
         PPAOTSLateUnits)
 # View(Monthly_Brand_Category_OTS)

# Create Monthly OTS Brand Table ----
Monthly_Brand_OTS <- OTS_Master %>%
  filter(OTS_Master$Week <= EOW) %>%
  group_by(Month_Number, ReportingBrand) %>% 
  summarise("OTSUnits" = floor(sum(Units)),
            "OTSOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "OTSLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "OTSLate5daysUnits" = floor(sum(Units[Lateness=="Late" & Days_Late > 5])), 
            "WTOTSLateUnits" = floor(sum(Units[Lateness=="Late"]*Days_Late[Lateness=="Late" & Days_Late >=1])),
            "PPAOTSLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]))) %>%  
  select(Month_Number, 
         ReportingBrand, 
         OTSUnits, 
         OTSOnTimeUnits, 
         OTSLateUnits, 
         OTSLate5daysUnits, 
         WTOTSLateUnits, 
         PPAOTSLateUnits)
# View(Monthly_Brand_OTS)

# Create Monthly OTS Category Table ----
Monthly_Category_OTS <- OTS_Master %>%
  filter(OTS_Master$Week <= EOW) %>%
  group_by(Month_Number, Category) %>% 
  summarise("OTSUnits" = floor(sum(Units)),
            "OTSOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "OTSLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "OTSLate5daysUnits" = floor(sum(Units[Lateness=="Late" & Days_Late > 5])), 
            "WTOTSLateUnits" = floor(sum(Units[Lateness=="Late"]*Days_Late[Lateness=="Late" & Days_Late >=1])),
            "PPAOTSLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]))) %>%  
  select(Month_Number, 
         Category, 
         OTSUnits, 
         OTSOnTimeUnits, 
         OTSLateUnits, 
         OTSLate5daysUnits, 
         WTOTSLateUnits, 
         PPAOTSLateUnits)
# View(Monthly_Category_OTS)

# Create Monthly Gap Inc OTS Table ----
Monthly_GapInc_OTS <- OTS_Master %>%
  filter(OTS_Master$Week <= EOW) %>%
  group_by(Month_Number) %>% 
  summarise("OTSUnits" = sum(Units),
            "OTSOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "OTSLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "OTSLate5daysUnits" = floor(sum(Units[Lateness=="Late" & Days_Late > 5])), 
            "WTOTSLateUnits" = floor(sum(Units[Lateness=="Late"]*Days_Late[Lateness=="Late" & Days_Late >=1])),
            "PPAOTSLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]))) %>%  
  select(Month_Number, 
         OTSUnits, 
         OTSOnTimeUnits, 
         OTSLateUnits, 
         OTSLate5daysUnits, 
         WTOTSLateUnits, 
         PPAOTSLateUnits)
 # View(Monthly_GapInc_OTS)

# Create Monthly - Preferred Vendor - New ----
Monthly_Preferred_Vendor_new <- inner_join(SOT_Master, Preferred_Vendor_new, by = c("Category"= "New Category", "Parent_Vendor"="Vendor Name")) %>% 
  filter(ShipCancelWeek <= EOW) %>%
  group_by(Category, Parent_Vendor, ShipCancelMonth) %>% 
  summarise("SOTUnits" = floor(sum(Units)),
            "SOTOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "SOTLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "SOTLate5daysUnits" = floor(sum(Units[Lateness=="Late" & DAYS_LATE > 5])), 
            "WTSOTLateUnits" = floor(sum(Units[Lateness=="Late"]*DAYS_LATE[Lateness=="Late" & DAYS_LATE >=1])),
            "PPAUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir"])),
            "PPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"])), 
            "PPASOT5daysLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE>5])),
            "WTPPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]*DAYS_LATE[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE >=1]))) %>%  
  select(Category,
         Parent_Vendor,
         ShipCancelMonth,
         SOTUnits, 
         SOTOnTimeUnits, 
         SOTLateUnits, 
         SOTLate5daysUnits, 
         WTSOTLateUnits, 
         PPAUnits,
         PPASOTLateUnits,
         PPASOT5daysLateUnits,
         WTPPASOTLateUnits) %>% 
  arrange(ShipCancelMonth, Category)
# Create Monthly Preferred Vendor - New OTS ----
Monthly_Preferred_Vendor_New_OTS <- inner_join(OTS_Master, Preferred_Vendor_new, by = c("Category"= "New Category", "Parent_Vendor"="Vendor Name")) %>% 
  filter(Week <= EOW) %>%
  group_by(Category, Parent_Vendor,  Month_Number ) %>% 
  summarise("OTSUnits" = floor(sum(Units)),
            "OTSOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "OTSLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "OTSLate5daysUnits" = floor(sum(Units[Lateness=="Late" & Days_Late > 5])), 
            "WTOTSLateUnits" = floor(sum(Units[Lateness=="Late"]*Days_Late[Lateness=="Late" & Days_Late >=1])),
            "PPAOTSLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]))) %>%  
  select(Category, 
         Parent_Vendor,
         Month_Number,
         OTSUnits, 
         OTSOnTimeUnits, 
         OTSLateUnits, 
         OTSLate5daysUnits, 
         WTOTSLateUnits, 
         PPAOTSLateUnits) %>% 
  arrange(Month_Number, Category)
# View(Monthly_Preferred_Vendor_New_OTS)


# Create Monthly Brand and Category Combine Table ----
Monthly_Brand_Category_Combine <- left_join(Monthly_Brand_Category_SOT, Monthly_Brand_Category_OTS, by= c("ShipCancelMonth"="Month_Number", "ReportingBrand"="ReportingBrand", "Category"="Category"))
Monthly_Brand_Category_Combine <- Monthly_Brand_Category_Combine[c(1:8, 13:17,9:10,18,11:12)]

# Create Monthly SOT Brand Combine Table ----
Monthly_Brand_Combine <- left_join(Monthly_Brand_SOT, Monthly_Brand_OTS, by= c("ShipCancelMonth"="Month_Number", "ReportingBrand"="ReportingBrand"))
Monthly_Brand_Combine <- Monthly_Brand_Combine[c(1:7, 12:16, 8:9, 17, 10:11)]
# View(Monthly_Brand_Combine)

# Create Monthly SOT Category Combine Table ----
Monthly_Category_Combine <- left_join(Monthly_Category_SOT, Monthly_Category_OTS, by= c("ShipCancelMonth"="Month_Number", "Category"="Category"))
Monthly_Category_Combine <- Monthly_Category_Combine[c(1:7, 12:16, 8:9, 17, 10:11)]
# View(Monthly_Category_Combine)

# Create Monthly SOT Gap Inc Combine Table ----
Monthly_GapInc_Combine <- left_join(Monthly_GapInc_SOT, Monthly_GapInc_OTS, by= c("ShipCancelMonth"="Month_Number"))
Monthly_GapInc_Combine <- Monthly_GapInc_Combine[c(1:6, 11:15,7:8,16,9:10)]
# View(Monthly_GapInc_Combine)
# Create Preferred Vendor Combine Table ----
Preferred_Vendor_New_Combine <- left_join(Monthly_Preferred_Vendor_new, Monthly_Preferred_Vendor_New_OTS, by = c("Category"="Category", "Parent_Vendor"="Parent_Vendor", "ShipCancelMonth" = "Month_Number"))
Preferred_Vendor_New_Combine <-Preferred_Vendor_New_Combine[c(1:8, 13:17, 9:10, 18, 11:12)]

# Create Monthly - byDC
Monthly_by_DC <- OTS_Master %>% 
  filter(OTS_Master$Week <= EOW) %>%
  group_by(Fiscal_Month, Month_Number, DCCampus, DC_NAME, DestCtryCD) %>% 
  summarise("Total Units" = floor(sum(Units)),
            "OnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "OTS%" = sum(Units[Lateness=="OnTime"])/sum(Units),
            "OTSLate5daysUnits" = floor(sum(Units[Lateness=="Late" & Days_Late > 5])),
            "WTOTSLateUnits" = floor(sum(Units[Lateness=="Late"]*Days_Late[Lateness=="Late" & Days_Late >=1])),
            "LateUnits"= floor(sum(Units[Lateness=="Late"]))) %>% 
  select(Fiscal_Month, 
         Month_Number, 
         `OnTimeUnits`, 
         `Total Units`, 
         DCCampus, 
         DC_NAME, 
         DestCtryCD, 
         `OTS%`, 
         `OTSLate5daysUnits`,
         `WTOTSLateUnits`, 
         `LateUnits`)
# Create Top 20 Countries SOT ----
Monthly_Top_20_SOT <- inner_join(SOT_Master, Top_20_Countries, by = c("CountryOfOrigin"= "CountryOfOrigin"))
Monthly_Top_20_SOT <- Monthly_Top_20_SOT %>%
  filter(ShipCancelWeek <= EOW) %>%
  group_by(ShipCancelMonth) %>% 
  summarise("SOTUnits" = floor(sum(Units)),
            "SOTOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "SOTLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "SOTLate5daysUnits" = floor(sum(Units[Lateness=="Late" & DAYS_LATE > 5])), 
            "WTSOTLateUnits" = floor(sum(Units[Lateness=="Late"]*DAYS_LATE[Lateness=="Late" & DAYS_LATE >=1])),
            "PPAUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir"])),
            "PPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"])), 
            "PPASOT5daysLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE>5])),
            "WTPPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]*DAYS_LATE[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE >=1]))) %>%  
  select(ShipCancelMonth, 
         SOTUnits, 
         SOTOnTimeUnits, 
         SOTLateUnits, 
         SOTLate5daysUnits, 
         WTSOTLateUnits, 
         PPAUnits,
         PPASOTLateUnits,
         PPASOT5daysLateUnits,
         WTPPASOTLateUnits)
# View(Monthly_Top_20_SOT) 

# Create Monthly Top 20 OTS Table ----
Monthly_TOP_20_OTS <- inner_join(OTS_Master, Top_20_Countries, by = c("ORIGIN_COUNTRY_CODE"="CountryOfOrigin"))
Monthly_TOP_20_OTS <- Monthly_TOP_20_OTS %>%
  filter(Week <= EOW) %>%
  group_by(Month_Number) %>% 
  summarise("OTSUnits" = sum(Units),
            "OTSOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "OTSLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "OTSLate5daysUnits" = floor(sum(Units[Lateness=="Late" & Days_Late > 5])), 
            "WTOTSLateUnits" = floor(sum(Units[Lateness=="Late"]*Days_Late[Lateness=="Late" & Days_Late >=1])),
            "PPAOTSLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]))) %>%  
  select(Month_Number, 
         OTSUnits, 
         OTSOnTimeUnits, 
         OTSLateUnits, 
         OTSLate5daysUnits, 
         WTOTSLateUnits, 
         PPAOTSLateUnits)
#  View(Monthly_TOP_20_OTS)
# Create Monthly Top 20 Combine table ----
Monthly_Top_20_Combine <- inner_join(Monthly_Top_20_SOT, Monthly_TOP_20_OTS, by= c("ShipCancelMonth"="Month_Number"))
Monthly_Top_20_Combine <- Monthly_Top_20_Combine[c(1:6, 11:15,7:8,16,9:10)]
  
# Create Monthly Top 50 Vendors SOT Table ----
Monthly_Top_50_Vendors_SOT <- inner_join(SOT_Master, Top_50_Vendors, by = c("Parent_Vendor"= "Parent_Vendor"))
Monthly_Top_50_Vendors_SOT <- Monthly_Top_50_Vendors_SOT %>%
  filter(ShipCancelWeek <= EOW) %>%
  group_by(ShipCancelMonth) %>% 
  summarise("SOTUnits" = floor(sum(Units)),
            "SOTOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "SOTLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "SOTLate5daysUnits" = floor(sum(Units[Lateness=="Late" & DAYS_LATE > 5])), 
            "WTSOTLateUnits" = floor(sum(Units[Lateness=="Late"]*DAYS_LATE[Lateness=="Late" & DAYS_LATE >=1])),
            "PPAUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir"])),
            "PPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"])), 
            "PPASOT5daysLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE>5])),
            "WTPPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]*DAYS_LATE[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE >=1]))) %>%  
  select(ShipCancelMonth, 
         SOTUnits, 
         SOTOnTimeUnits, 
         SOTLateUnits, 
         SOTLate5daysUnits, 
         WTSOTLateUnits, 
         PPAUnits,
         PPASOTLateUnits,
         PPASOT5daysLateUnits,
         WTPPASOTLateUnits)
 # View(Monthly_Top_50_Vendors_SOT) 
# Create Monthly Top 50 Vendors OTS Table ----
Monthly_Top_50_Vendors_OTS <- inner_join(OTS_Master, Top_50_Vendors, by = c("Parent_Vendor"= "Parent_Vendor"))
Monthly_Top_50_Vendors_OTS <- Monthly_Top_50_Vendors_OTS %>%
  filter(Week <= EOW) %>%
  group_by(Month_Number) %>% 
  summarise("OTSUnits" = sum(Units),
            "OTSOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
            "OTSLateUnits"= floor(sum(Units[Lateness=="Late"])),
            "OTSLate5daysUnits" = floor(sum(Units[Lateness=="Late" & Days_Late > 5])), 
            "WTOTSLateUnits" = floor(sum(Units[Lateness=="Late"]*Days_Late[Lateness=="Late" & Days_Late >=1])),
            "PPAOTSLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]))) %>%  
  select(Month_Number, 
         OTSUnits, 
         OTSOnTimeUnits, 
         OTSLateUnits, 
         OTSLate5daysUnits, 
         WTOTSLateUnits, 
         PPAOTSLateUnits)
 # View(Monthly_GapInc_OTS)


# Create Monthly SOT Top 50 Vendors Combine Table ----
Monthly_Top_50_Vendors_Combine <- left_join(Monthly_Top_50_Vendors_SOT, Monthly_Top_50_Vendors_OTS, by= c("ShipCancelMonth"="Month_Number"))
Monthly_Top_50_Vendors_Combine <- Monthly_Top_50_Vendors_Combine[c(1:6, 11:15,7:8,16,9:10)]
# View(Monthly_Top_50__Vendors_Combine)
# Create OTSvsSOT table ----
 OTS_vs <- OTS_Master %>% 
   select(NUMBER_SEQ, Month_Number, Lateness, Units) %>%
   filter(Lateness!= "Undetermined") %>% 
   rename("StockedOnTime" = Lateness) %>%
   group_by(Month_Number, StockedOnTime) %>% 
   droplevels()
 
 SOT_vs <- SOT_Master %>% 
   select(NUMBER_SEQ, ShipCancelMonth, Lateness) %>%
   filter(Lateness!="Unmeasured") %>% 
   rename("ShippedOnTime" = Lateness) %>%
   group_by(ShipCancelMonth, ShippedOnTime) %>% 
   droplevels()
 
 OTSvsSOT <- inner_join(OTS_vs, SOT_vs, by = c("NUMBER_SEQ"= "NUMBER_SEQ")) %>%
   group_by(Month_Number, StockedOnTime, ShippedOnTime) %>% 
   summarise("SumOfUnits" = floor(sum(Units)))
 
# Create Monthly Brand Top 10 Delay Combine ----
Brand_Top_Ten_Delay <-  SOT_Master %>% 
   filter(ShipCancelWeek <= EOW) %>%
   group_by(ReportingBrand, ShipCancelMonth, Parent_Vendor) %>% 
   summarise("SOTUnits" = floor(sum(Units)),
             "AdjustedSOTUnits"= floor(sum(Units[Lateness=="OnTime"]) + sum(Units[Lateness=="Late"])),
             "SOTOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
             "SOTLateUnits"= floor(sum(Units[Lateness=="Late"])),
             "SOTLate5daysUnits" = floor(sum(Units[Lateness=="Late" & DAYS_LATE > 5])), 
             "WTSOTLateUnits" = floor(sum(Units[Lateness=="Late"]*DAYS_LATE[Lateness=="Late" & DAYS_LATE >=1])),
             "PPAUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir"])),
             "PPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"])), 
             "PPASOT5daysLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE>5])),
             "WTPPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]*DAYS_LATE[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE >=1]))) %>%  
   select(
          ReportingBrand,
          ShipCancelMonth,
          Parent_Vendor,
          SOTUnits,
          AdjustedSOTUnits,
          SOTOnTimeUnits, 
          SOTLateUnits, 
          SOTLate5daysUnits, 
          WTSOTLateUnits) %>% 
   top_n(10, SOTLateUnits) %>% 
   arrange(ReportingBrand,ShipCancelMonth, desc(SOTLateUnits))
   
# Create Monthly Category Top 10 Delay Combine ----
Category_Top_Ten_Delay <-  SOT_Master %>% 
   filter(ShipCancelWeek <= EOW) %>%
   group_by(Category, ShipCancelMonth, Parent_Vendor) %>% 
   summarise("SOTUnits" = floor(sum(Units)),
             "AdjustedSOTUnits"= floor(sum(Units[Lateness=="OnTime"]) + sum(Units[Lateness=="Late"])),
             "SOTOnTimeUnits" = floor(sum(Units[Lateness=="OnTime"])),
             "SOTLateUnits"= floor(sum(Units[Lateness=="Late"])),
             "SOTLate5daysUnits" = floor(sum(Units[Lateness=="Late" & DAYS_LATE > 5])), 
             "WTSOTLateUnits" = floor(sum(Units[Lateness=="Late"]*DAYS_LATE[Lateness=="Late" & DAYS_LATE >=1])),
             "PPAUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir"])),
             "PPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"])), 
             "PPASOT5daysLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE>5])),
             "WTPPASOTLateUnits" = floor(sum(Units[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late"]*DAYS_LATE[SHP_MODE_CATG_NM == "PrepaidAir" & Lateness=="Late" & DAYS_LATE >=1]))) %>%  
   select(
          Category,
          ShipCancelMonth,
          Parent_Vendor,
          SOTUnits,
          AdjustedSOTUnits,
          SOTOnTimeUnits, 
          SOTLateUnits, 
          SOTLate5daysUnits, 
          WTSOTLateUnits) %>% 
   top_n(10, SOTLateUnits) %>% 
   arrange(Category, ShipCancelMonth, desc(SOTLateUnits))
   
# Write tables ----
write_csv(Monthly_Brand_Category_Combine, 
          path = paste(SOT_OTS_directory,  
                       paste('Monthly_Brand_Category_Combine_WE_', EOW, '.csv',sep = ""), sep = '/' ))
write_csv(Monthly_Brand_Combine, 
          path = paste(SOT_OTS_directory,  
                       paste('Monthly_Brand_Combine_WE_', EOW, '.csv',sep = ""), sep = '/' ))
write_csv(Monthly_Category_Combine, 
          path = paste(SOT_OTS_directory,  
                       paste('Monthly_Category_Combine_WE_', EOW, '.csv',sep = ""), sep = '/' ))
write_csv(Monthly_GapInc_Combine, 
          path = paste(SOT_OTS_directory,  
                       paste('Monthly_GapInc_Combine_WE_', EOW, '.csv',sep = ""), sep = '/' ))
write_csv(Preferred_Vendor_New_Combine, 
          path = paste(SOT_OTS_directory,  
                       paste('Preferred_Vendor_New_Combine_WE_', EOW, '.csv',sep = ""), sep = '/' ))
write_csv(Monthly_by_DC, 
          path = paste(SOT_OTS_directory,  
                       paste('Monthly_by_DC_WE_', EOW, '.csv',sep = ""), sep = '/' ))
write_csv(Monthly_Top_20_Combine, 
          path = paste(SOT_OTS_directory,  
                       paste('Monthly_Top_20_Countries_WE_', EOW, '.csv',sep = ""), sep = '/' ))
write_csv(Monthly_Top_50_Vendors_Combine, 
          path = paste(SOT_OTS_directory,  
                       paste('Monthly_Top_50_Vendors_WE_', EOW, '.csv',sep = ""), sep = '/' ))
write_csv(OTSvsSOT, 
          path = paste(SOT_OTS_directory,  
                       paste('OTSvsSOT_WE_', EOW, '.csv',sep = ""), sep = '/' ))
write_csv(Brand_Top_Ten_Delay, 
          path = paste(SOT_OTS_directory,  
                       paste('Brand_Top_10_Delay_WE_', EOW, '.csv',sep = ""), sep = '/' ))
write_csv(Category_Top_Ten_Delay, 
          path = paste(SOT_OTS_directory,  
                       paste('Category_Top_10_Delay_WE_', EOW, '.csv',sep = ""), sep = '/' ))